Efficient map matching method for autonomous driving and apparatus thereof

ABSTRACT

A map matching method for autonomous driving includes extracting a first statistical map from 3D points contained in 3D map data; extracting a second statistical map from 3D points of surroundings which are obtained by a detection sensor simultaneously or after the previous extracting of the statistical map; dividing the second statistical map into a vertical-object part and a horizontal-object part; and performing map matching using the horizontal-object part and/or the vertical-object part and the first statistical map.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 16/830,867, filed Mar. 26, 2020 and entitled “EFFICIENT MAPMATCHING METHOD FOR AUTONOMOUS DRIVING AND APPARATUS THEREOF,” whichclaims priority under 35 U.S.C. § 119 to Korean Patent Application No.10-2019-0166900, filed on Dec. 13, 2019 in the Korean IntellectualProperty Office (KIPO), the contents of each of which are hereinincorporated by reference in their entirety.

FIELD OF TECHNOLOGY

The present disclosure relates to a map matching method for autonomousdriving and an apparatus thereof, and more specifically, to a method forestimating a pose of an autonomous driving apparatus by comparing 3Ddata of surroundings that is input from a detection sensor with 3D mapdata stored in advance, and the apparatus thereof.

BACKGROUND

Autonomous driving is a technique in which an autonomous drivingapparatus itself recognizes a driving environment by using a sensor,autonomously operates a drive system, and performs autonomous driving toa target point without an operation performed by a driver.

The autonomous driving apparatus basically needs to recognize itsposition in a driving environment for autonomous driving.

In order to perform such recognition, 3D data of surroundings recognizedby a detection sensor installed on the autonomous driving apparatus iscompared with 3D map data stored in advance, and pose estimation orlocalization of the autonomous driving apparatus is performed within a3D map. Here, this process is referred to as map matching. Theautonomous driving is performed based on the pose of the autonomousdriving apparatus, and thus there is a demand for a map matchingtechnique which is performed in real time with high accuracy.

As one map matching method, features of a tree, a building, a road, andthe like which can be used as a landmark are extracted in advance in 3Ddata of surroundings recognized by a detection sensor such that anextracted feature part can be compared with a corresponding part on a 3Dmap; however, this method has a disadvantage in that an amount ofprocessing increases in a process of extracting the features from thedetected 3D data.

As another map matching method, reflectivity of a measurement surface isused in map matching, the reflectivity being obtained by a lightdetection and ranging (LiDAR) sensor as a detection sensor whichmeasures a feature of a return signal reflected after a target object isirradiated with an optical pulse. However, the reflectivity of thesurface in an external environment such as a road is a feature thatsignificantly changes depending on weather such as rain or snow, andthus the map matching method has a problem of a change in accuracydepending on weather.

In general, a pose of an autonomous driving apparatus can be identifiedin map matching by comparing a 3D structure recognized by a detectionsensor with 3D map data. Pose identification using the 3D structure canbe performed with consideration for 3D spatial coordinates (for example,x, y, and z in a case of using an orthogonal coordinate system)representing the pose of the autonomous driving apparatus and 3Drotation angles (roll, pitch, and yaw) representing an attitude of theautonomous driving apparatus. This is referred to assix-degree-of-freedom (DOF) pose estimation. In this case, the amount ofprocessing is proportional to a search space to the sixth power for eachdegree of freedom, and thus the estimation has a drawback of a longprocessing time. In order to solve the drawback, a method for obtainingonly some of the six degrees of freedom (for example, three degrees offreedom) by using the map matching and estimating the remaining degreesof freedom by a motion sensor or the like can be employed; however, thismethod has a drawback in that a pose error occurs. Consequently, thereis a demand for a new map matching method by which it is possible tominimize a pose error of an autonomous driving apparatus while theamount of processing for map matching is reduced.

In addition, whereas items of 3D map data used in map matching indicatestatic elements of surrounding environments of an autonomous drivingapparatus, a detection sensor recognizes, in a real driving environment,a temporary object such as a passerby, a car, or a temporarily installedobject that is not reflected in the 3D map data, and thus accuracy ofthe map matching is lowered when 3D surrounding data containing thetemporary object is compared with the 3D map data. In order to solvesuch a problem, there is a demand for a new map matching method thatincludes a process for removing a part corresponding to the temporaryobject in advance from the 3D surrounding data recognized by thedetection sensor.

Consequently, there is a demand for a new map matching method forremoving a temporary object in advance from 3D surrounding data obtainedby a detection sensor in order to improve accuracy of the map matchingand for performing processing in real time while minimizing a pose errorin the map matching for autonomous driving, and the present disclosureis provided with consideration for the demand.

SUMMARY

An object of the present disclosure is to provide a new map matchingmethod for optimizing an amount of processing such that processing canbe performed in real time while a pose error of an autonomous drivingapparatus is minimized.

In addition, another object of the present disclosure is to provide anefficient method for recognizing and removing a temporary object from 3Dsurrounding data recognized by a detection sensor of an autonomousdriving apparatus in order to estimate a pose of the autonomous drivingapparatus with accuracy.

In order to achieve the technical object described above, a map matchingmethod for autonomous driving according to an embodiment of the presentdisclosure includes: extracting a first statistical map from 3D pointsof 3D map data; extracting a second statistical map from 3D pointsobtained by a detection sensor of the autonomous driving apparatussimultaneously with or after the previous extraction of the firststatistical map; dividing the second statistical map into avertical-object part and a horizontal-object part; and estimating a poseod the autonomous driving apparatus by comparing the horizontal-objectpart and/or the vertical-object part and the first statistical map.

According to an embodiment of the present disclosure, in the statisticalmap, a 3D space may be divided in accordance with a predetermined rule,and one or more statistical values of 3D points contained in a dividedregion represent the 3D points in the divided region.

According to an embodiment of the present disclosure, in the statisticalmap, a 3D space may be divided with respect to a 2D plane, and one ofmore statistical values of the remaining dimension of 3D pointscontained in the divided region may be calculated and represented on the2D plane.

According to an embodiment of the present disclosure, in the statisticalmap, an (x, y) plane may be divided into evenly spaced grids in theCartesian coordinate system to represent one or more statistical valuesof height z values of 3D points corresponding to the respective grids.

According to an embodiment of the present disclosure, in the statisticalmap, statistical values of height z values may be represented incorresponding regions formed by dividing r and θ of an (r, θ) planeevenly in the cylindrical coordinate system.

According to an embodiment of the present disclosure, in the statisticalmap, statistical values of respective θ values of 3D points may berepresented in corresponding regions formed by dividing r and ϕ of an(r, ϕ) plane evenly in the spherical coordinate system.

According to an embodiment of the present disclosure, the statisticalmap may represent one or more statistical values of a mean, a mode, amaximum, a median, a minimum, a range, an interquartile range, aquartile deviation, a variance, a standard deviation, a coefficient ofvariation, and a covariance.

According to an embodiment of the present disclosure, whether 3D pointscontained in one of the divided regions are divided into two or moregroups in the statistical map may be detected.

According to an embodiment of the present disclosure, when the two ormore separated groups of 3D points are detected in a divided region inthe statistical map, the divided region may be divided again such thateach one of divided region contains one group of 3D points only.

According to an embodiment of the present disclosure, when the number of3D points contained in one of the two or more groups contained in one ofthe divided regions is equal to or smaller than a predetermined value,the 3D points contained in the group may be deleted.

According to an embodiment of the present disclosure, detecting whetherthe 3D points contained in each divided region are separated into two ormore groups may be forming groups by grouping 3D points positionedwithin a predetermined distance and checking whether the two or moregroups that are separated from each other by a distance more than thepredetermined length are formed.

According to an embodiment of the present disclosure, the secondstatistical map may be divided into the vertical-object part and thehorizontal-object part depending on statistical values of the respectivedivided regions of the second statistical map.

According to an embodiment of the present disclosure, regions of thestatistical map that have statistical values larger than a predeterminedvalue may be extracted as the vertical-object part, and the regions ofthe statistical map that have statistical values smaller than thepredetermined value may be extracted as the horizontal-object part, andthe vertical-object part may contain a nearby part having smallerstatistical values compared to the part having the statistical valueslarger than the predetermined value.

According to an embodiment of the present disclosure, in the performingof the map matching, the map matching may be performed based on theposition of the autonomous driving apparatus which is estimated usingdata obtained by a motion sensor.

According to an embodiment of the present disclosure, in the estimationof a pose of the autonomous driving apparatus, the first statistical mapmay be compared with the vertical-object part and/or thehorizontal-object part based on each point located in a search range,and the position having a highest mutual similarity may be estimated asa result.

According to an embodiment of the present disclosure, the positionhaving the highest mutual similarity may mean to have the lowest errorbetween the first statistical map and the vertical-object part and/orthe horizontal-object part.

According to an embodiment of the present disclosure, in the estimationof a pose of the autonomous driving apparatus, a map matching may beperformed using the first statistical map and the vertical-object part,and by using a result thereof, final map matching may be performed usingthe first statistical map and the horizontal-object part.

According to an embodiment of the present disclosure, in the estimationof a pose of the autonomous driving apparatus, the map matching may beperformed using the first statistical map and the horizontal-objectpart, and by using the result thereof, final map matching may beperformed using the first statistical map and the vertical-object part.

According to an embodiment of the present disclosure, in the estimationof a pose of the autonomous driving apparatus, map matching with respectto (h, r, p) may be performed using the first statistical map and thehorizontal-object part to store values of (r′, p′) which minimize errorswith respect to the respective h values, and map matching with respectto (x, y, h) may be performed using the map-statistical map and thevertical-object part to perform map matching with respect to (x, y, h,r′, p′) using values of (r′, p′) with respect to the respective h valuesstored in advance.

According to an embodiment of the present disclosure, in the estimationof a pose of the autonomous driving apparatus, after a map matchingperformed using the first statistical map and the horizontal-object partsimultaneously with a map matching performed using the first statisticalmap and the vertical-object part, the two map matching results may becombined to obtain a final map matching result.

According to an embodiment of the present disclosure, in the estimationof a pose of the autonomous driving apparatus, a map matching withrespect to (x, y, h) is performed using the first statistical map andthe vertical-object part to calculate a value of (x′, y′, h′) whichminimize errors, and simultaneously, a map matching of the firststatistical map and the horizontal-object part is performed with respectto (r, p, h) to store values of (r, p) which minimize errors withrespect to respective h values, and (x′, y′, h′, r′, p′) may beestimated as a final position by using (r′, p′) corresponding to h′.

According to an embodiment of the present disclosure, the map matchingusing the vertical-object part may be performed with respect to (x, y,h) or a part thereof, and the map matching using the horizontal-objectpart may be performed with respect to (z, h, r, p) or a part thereof.

According to an embodiment of the present disclosure, the map matchingmethod may further include removing a part corresponding to a temporaryobject from the 3D points obtained by the detection sensor, the secondstatistical map and/or the vertical-object part.

According to an embodiment of the present disclosure, the map matchingmethod may further include: acquiring coordinates of surrounding objectsaround the autonomous driving apparatus; and identifying the temporaryobject by comparing positions corresponding to the coordinates of thesurrounding objects in the 3D map data in order to identify the partcorresponding to the temporary object.

According to an embodiment of the present disclosure, the acquiring ofthe coordinates of the surrounding objects around the autonomous drivingapparatus may include grouping the points indicating the same object ofthe 3D points input from the detection sensor, and acquiring thecoordinate of the region that the same object of the grouped pointsoccupies.

According to an embodiment of the present disclosure, in the identifyingof the temporary object by comparing the positions corresponding to thecoordinates of the surrounding objects in the 3D map data, a spatialfeature of the corresponding coordinates may be found using statisticalvalues corresponding to the coordinates of the surrounding objects inthe first statistical map, and presence of a temporary object at thecorresponding coordinates may be identified based on the spatialfeature.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an autonomous vehicle as an embodiment of anautonomous driving apparatus according to the present disclosure.

FIG. 2 illustrates an example of a pose estimating method according tothe present disclosure.

FIGS. 3A and 3B conceptually illustrate a 3D structure and a statisticalmap thereof according to an embodiment of the present disclosure.

FIG. 4 illustrates fundamental process of a map matching according to anembodiment of the present disclosure.

FIG. 5 illustrates an embodiment according to the present disclosure inwhich a vertical-object part and a horizontal-object part are extractedfrom a second statistical map obtained by a detection sensor and eachpart is used in separate map matching process.

FIG. 6 illustrates embodiments according to the present disclosure, ofmethods for combining the two map matching results after map matchingsare performed in two different groups.

FIG. 7 illustrates a configuration in which a temporary object isremoved from surrounding data obtained by the detection sensor prior tothe map matching according to an embodiment of the present disclosure.

FIG. 8 illustrates a method for removing a temporary object and mapmatching according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

The present disclosure relates to a pose estimating method of theautonomous driving apparatus and the apparatus thereof, particularly, toan efficient pose estimating method for estimating an accurate pose ofthe autonomous driving apparatus within a 3D map in the autonomousdriving apparatus and processing the estimation of the accurate pose inreal time and the apparatus thereof.

FIG. 1 illustrates an autonomous vehicle 100 as an embodiment of theautonomous driving apparatus according to the present disclosure.

The autonomous vehicle 100 in FIG. 1 includes at least some of a lightdetection and ranging (lidar) sensor, a radio detection and ranging(radar) sensor, a sound navigation and ranging (sonar) sensor, a camera,a GPS sensor, and an inertial measurement unit (IMU). First, the lidar,the radar, and/or the sonar sensor may be installed as a rotary sensor110 at the top of a vehicle or as fixed sensors 111, 112, 113, 114, and115 which are all installed on respective surfaces of the vehicle tosense fixed directions. A stereo camera 120 may be installed on a frontside and/or a rear side of the vehicle or can be installed on each ofthe surfaces of the vehicle. The GPS sensor analyzes a signal from a GPSsatellite to estimate a location of the autonomous vehicle 100, and theIMU generally has built-in three-axis accelerometer and three-axisangular velocity meter and can measure acceleration in a forwarddirection x, a transverse direction y, and a height direction z andangular velocity of roll that is rotated around an x-axis, pitch that isrotated around a y-axis, and yaw that is rotated around a z-axis. Here,the yaw rotation around the z-axis represents a proceeding direction ofthe autonomous driving apparatus and, thus, is also called heading. TheIMU can calculate a velocity v and attitude angles (roll, pitch, andyaw) of the autonomous driving apparatus by using the measuredacceleration and angular velocity.

FIG. 2 illustrates an example of a pose estimating method according tothe present disclosure.

In the pose estimating method according to the present disclosure, aprocess for estimating a pose X_(t) of the autonomous driving apparatusat a time of t from a pose X_(t-1) of the autonomous driving apparatusat a time of t−1 is repeated. In a case of using an orthogonalcoordinate system as an example, the pose X_(t) of the autonomousdriving apparatus is obtained by Equation (1) in which a pose (x_(t),y_(t), z_(t)) and an attitude (r_(t), p_(t), h_(t)) of the autonomousdriving apparatus in a three dimension are expressed as a vector.

X _(t) =[x _(t) ,y _(t) ,z _(t) ,r _(t) ,p _(t) ,h _(t)]  Equation (1)

Here, (x_(t), y_(t), z_(t)) is a coordinate indicating, in theorthogonal coordinate system, a pose (x, y, z) of the autonomous drivingapparatus in the three dimension, and (r_(t), p_(t), h_(t)) represents arotation angle indicating the attitude (roll, pitch, heading) of theautonomous driving apparatus in the three dimension at a time t. Here,r_(t) represents the rotation angle (roll) around a forward direction xof the autonomous driving apparatus, p_(t) represents the rotation angle(pitch) around an axis y which is horizontally orthogonal to the forwarddirection of the autonomous driving apparatus, h_(t) represents ahorizontal forward direction (heading) of the autonomous drivingapparatus, as the rotation angle (yaw) around an axis z which isvertically orthogonal to the forward direction of the autonomous drivingapparatus.

First, the basic 3D pose (x_(t), y_(t), z_(t)) and attitude (r_(t),p_(t), h_(t)) of the autonomous driving apparatus 100 are primarilyestimated from a result obtained from a motion sensor. Here, the motionsensor may use the GPS sensor and/or the IMU as an embodiment thereof.For example, when velocity v_(t) at a time t and the 3D attitude (r_(t),p_(t), h_(t)) of the autonomous driving apparatus are obtained from theIMU, the autonomous driving apparatus is assumed moving at a velocity ofv_(t) while maintaining the 3D attitude (r_(t), p_(t), h_(t)), and thusit is possible to estimate the 3D pose (x_(t), y_(t), z_(t)). However,the 3D pose and attitude obtained from the IMU contains an errorgenerated in the inertial measurement unit and, thus, does not meet adegree of accuracy suitable for autonomous driving. Hence, the errorgenerated in preliminary estimation is minimized through the followingmap matching process. As described above, the basic map matching processon the basis of the orthogonal coordinate system is described; however,the map matching process is only an embodiment of the presentdisclosure, and the map matching process according to the presentdisclosure is not limited to the orthogonal coordinate system and can berealized by another coordinate system or another description method.

Here, the preliminary estimation of the 3D pose and attitude of theautonomous driving apparatus using the motion sensor is performed toreduce a search space that needs to be considered in the map matchingprocess, and the preliminary estimation thereof is not a necessaryprocess. In other words, when the autonomous driving apparatus hassufficient processing power, the preliminary estimation is omitted, andX_(t) can be directly estimated by applying the map matching withX_(t-1) as a reference. In other words, according to an embodiment ofthe present disclosure, after the preliminary estimation is performedusing the motion sensor, a pose of the autonomous driving apparatus canbe determined using the map matching. In addition, according to anotherembodiment of the present disclosure, a pose of the autonomous drivingapparatus can be determined using the map matching without a preliminaryestimation process.

Next, in the map matching process, 3D information of surroundings aroundthe autonomous driving apparatus 100 obtained by a detection sensor iscompared with 3D map data stored in advance, and the pose of theautonomous driving apparatus is determined in a 3D map. Here, as anembodiment of the detection sensor, a lidar sensor, a radar sensor,and/or a sonar sensor can be used. In addition, 3D information of thesurroundings can be obtained using 3D image information obtained from acamera as another embodiment of the detection sensor. In one embodimentaccording to the present disclosure, as the detection sensor, one of thelidar sensor, the radar sensor, the sonar sensor, or the camera can beused. In addition, in another embodiment according to the presentdisclosure, as the detection sensor, two or more sensors of the lidarsensor, the radar sensor, the sonar sensor, and the camera can be used,and detection results of the sensors can be combined to be used as aresult of the detection sensor.

Items of 3D data of the surroundings obtained from the detection sensorare information of a surrounding object positioned in a 3D space,represent a 3D coordinate at which the object is detected or a physicalquantity measured at the corresponding coordinate, and are referred toas 3D points in the present disclosure.

According to one embodiment of the present disclosure, the presentdisclosure can include a process for converting 3D points contained inthe 3D map data before the map matching and/or 3D points obtained fromthe detection sensor into a statistical map. The 3D points obtained fromthe 3D map data or the detection sensor are related to a 3D space andare data represented by 3D coordinates. In the statistical map, a 3Dcoordinate system is divided in accordance with a predetermined rule,and statistical values of the points contained in respective dividedregions are obtained such that the 3D information is statisticallydescribed using the statistical values. As the statistical value thatcan be used in the statistical map, a mean, a mode, a maximum, a median,a minimum, a range, an interquartile range, a quartile deviation, avariance, a standard deviation, a coefficient of variation, acovariance, or the like can be used; however, the statistical value isnot limited thereto, and one or more values can be selected to be usedfrom all possible statistical values.

In an embodiment of the statistical map, a 3D space can be divided intoregular hexahedrons having an equal size, and statistical values of 3Dpoints contained in respective regular hexahedrons are calculated suchthat the 3D information can be displayed. A use of the statistical mapis advantageous in that the 3D data decreases in size, and an amount ofprocessing for comparing the 3D map data with 3D surrounding data isreduced in the map matching process.

As another embodiment of the statistical map, a 3D space can be dividedwith respect to a 2D plane, statistical values can be calculated withrespect to the remaining dimension, and 3D information can be displayedas the statistical map projected on the 2D plane. In an example of anorthogonal coordinate system (x, y, z), x and y axes on an (x, y) planecan be divided into grids at regular intervals, statistical values of zvalues of 3D points corresponding to the respective grids can becalculated, and 3D information can be displayed as the statistical map.For example, means of the z values corresponding to respective grids canbe calculated and can be displayed on the (x, y) plane to generate thestatistical map. As another embodiment, a (y, z) plane can be dividedinto grids at regular intervals, values (median or standard deviation)of x values of 3D points corresponding to the respective grids can becalculated, and 3D information can be displayed as the statistical map.In this case, a statistical map can be obtained, in which the 3Dinformation is represented by statistical values on the 2D plane, and ause of the statistical map is effective in that the amount of processingfor map matching is reduced.

As another embodiment of the statistical map, an (r, θ) plane can bedivided using a cylindrical coordinate system (r, θ, z) in accordancewith a determined rule such that statistical values of z values can bedisplayed. For example, regions divided from the (r, θ) plane can havean arc shape with a thickness, and modes of z values of 3D pointscorresponding to the respective regions can be calculated and displayedas the statistical map. As another embodiment according to the presentdisclosure, a spherical coordinate system (r, θ, ϕ) can be used, the 2Dplane in this case can divide (r, ϕ) plane into regions having an arcshape with a thickness, and the statistical map can be described bystatistical values of 0 corresponding to the respective divided regions.Besides, various coordinate systems can be used, and various methods fordividing the 2D plane into a plurality of regions can be used.

In addition, according to another embodiment of the present disclosure,in order to obtain a statistical map, every region can be divided inaccordance with one rule as a rule of division of a 3D coordinate, andthe 3D coordinate can be divided into two or more regions and therespective regions can be divided in accordance with different rules. Inaddition, in a case where the 3D coordinate is divided with respect tothe 2D plane, all of the regions can be divided in accordance with onerule as necessary, or the 2D plane can be divided into certain rangesand the respective ranges can be divided in accordance with differentrules.

In addition, according to still another embodiment of the presentdisclosure, in a process for dividing the 3D coordinate in order toobtain the statistical map, a 3D coordinate is primarily divided, the 3Dpoints contained in respective divided regions are analyzed, and theregions can be additionally divided in a case where the regions need tobe divided.

FIG. 3 conceptually illustrates a 3D structure and a statistical mapthereof according to an embodiment of the present disclosure.

FIG. 3 describe a concept of the statistical map on the basis of anorthogonal coordinate system (x, y, z). In an embodiment illustrated inFIG. 3 , an x axis and a y axis are divided to form evenly spaced gridson an (x, y) plane, each grids are considered a divided region, andmeans and variances of height z values of regions corresponding torespective grids in a 3D map data are calculated and stored as valuesfor the respective grids.

For example, when a 3D structure having a shape illustrated in FIG. 3(a)is present in a 3D map, the means and variances of height valuesrepresented in the 3D map which correspond to the respective grids arecalculated and stored as values for the respective grids. As a result,in the statistical map, values (means and variances) are stored for therespective grids, and the 3D structure stored in the 3D map issimplified and illustrated with means and variances of heights of 2Dgrids as in FIG. 3(b).

Conversion of the 3D map data and/or the 3D surrounding data into thestatistical map as illustrated in FIG. 3(b) is advantageous in that atotal amount of data decreases, and an amount of processing forcomparison to perform the map matching decreases. In addition, height zis a feature of the 3D structure in 3D surrounding information for theautonomous driving apparatus, and thus there is no great difference inaccuracy even when the statistical map illustrated in FIG. 3B isextracted and used.

In addition, according to an embodiment of the present disclosure, inconversion of 3D surrounding data obtained from the 3D map data and/orthe detection sensor into the statistical map, it is possible to checkwhether 3D points contained in one of the divided regions are dividedinto two or more groups. It is possible to check whether the 3D pointspositioned within a predetermined distance are grouped to form a groupand two or more groups that are separated from each other by a distancemore than the predetermined distance are formed. The two or more groupsobtained through such a process are groups of points of which theclosest 3D points are positioned at a distance longer than thepredetermined distance.

According to an embodiment of the present disclosure, when the two ormore divided groups are found in the one divided region, the one dividedregion can be divided again into regions such that one group is presentin the one divided region.

According to another embodiment of the present disclosure, when two ormore divided groups are found, the number of points that are containedin each group is checked, and the group can be determined as a kind ofoutlier and can be deleted when the number is smaller than a certainvalue. In addition, only when the number of points contained in eachgroup is greater than the certain value, the groups are determined asseparate objects, a space in which the corresponding groups are presentis divided again such that a new region is defined, and statisticalvalues for the corresponding regions can be calculated and displayed asthe statistical map.

For example, regarding a traffic signal positioned on a road, a columnpart is positioned on a sidewalk, and a traffic signal part is extendedinto the air from the column and is positioned above the road. In a casewhere a 3D space is divided with respect to the (x, y) plane, and thestatistical map is configured of statistical values of heights of 3Dpoints contained in a region divided into 2D grids, when a statisticalvalue of heights corresponding to 2D grids over the road at which thetraffic signal part is positioned in the air is extracted, a statisticalvalue of both a road part and the traffic signal part positioned in theair is calculated. In this case, the calculated statistical value is notconsidered to reflect the actual 3D structure.

According to an embodiment of the present disclosure, when grouping of3D points positioned within a certain distance and presence of two ormore groups are checked, it can be checked whether a group correspondingto the road and a group corresponding to the traffic signal part isdivided, and the region can be divided into two regions that contain therespective groups. According to another embodiment of the presentdisclosure, when the number of points that are contained in each checkedgroup is smaller than a predetermined value, the group can be determinedas an outlier and can be simply removed.

FIG. 4 illustrates fundamental process of map matching according to anembodiment of the present disclosure. FIG. 4 and the followingdescription are provided on the basis of the orthogonal coordinatesystem; however, FIG. 4 and the following description are provided onlyas an embodiment, and the map matching of the present disclosure is notlimited to FIG. 4 and the following description.

The map matching process illustrated in FIG. 4 according to anembodiment of the present disclosure is basically performed byextracting respective statistical maps from the 3D map data and the 3Dsurrounding data obtained by the detection sensor, assuming that theautonomous driving apparatus is present at an estimated pose (x′, y′,z′, r′, p′, h′) within a search space, calculating an error by comparingthe two statistical maps, repeating these processes by changingestimated poses of the autonomous driving apparatus, and estimating thepose (x′, y′, z′, r′, p′, h′) by which the error is minimized as a poseof the autonomous driving apparatus.

According to an embodiment of the present disclosure illustrated in FIG.4 , a certain range from a preliminary estimated pose of the autonomousdriving apparatus is set as a search space, the autonomous drivingapparatus is assumed to be present at all of the poses contained in thesearch space, and a process for calculating an error by comparing thetwo statistical maps can be repeated. In an embodiment according to thepresent disclosure, a pose of the autonomous driving apparatus which isestimated from the motion sensor can be used as the preliminaryestimated pose. According to another embodiment of the presentdisclosure, the preliminary estimated pose can be an estimated pose(X_(t-1)) of the autonomous driving apparatus at (t−1).

In the process according to an embodiment of the present disclosureillustrated in FIG. 4 , the map matching is described to be performedwith consideration for all the six degrees of freedom (6 DOF) of theautonomous driving apparatus; however, according to another embodimentof the present disclosure, the degrees of freedom considered in the mapmatching can be reduced to reduce the amount of processing, or, afterthe six degrees of freedom are divided into two or more degrees offreedom and the map matching is performed, the map matching can beperformed by combining results thereof.

According to an embodiment of the present disclosure, a substantiallyaccurate value for a height z of the autonomous driving apparatus can becalculated by a method of adding a height of the autonomous drivingapparatus to a height of a ground surface corresponding to a 2Dcoordinate in the 3D map data, and this can be excluded in the matchingprocess and the map matching can be performed with respect to theremaining degrees of freedom. In addition, according to an embodiment ofthe present disclosure, a pose (x, y) on the 2D plane and a forwarddirection (h) are important for the autonomous driving apparatus, andthus the map matching can be performed with respect to only (x, y, h)with consideration for only three degrees of freedom (3 DOF).

In addition, according to another embodiment of the present disclosure,map matching can be divided into a plurality of map matching of three orless degrees of freedom, and respective map matching results can becombined to obtain a map matching result of six degrees of freedom.Here, the plurality of map matching can be divided into a plurality ofcombinations of map matching of degrees of freedom which are differentfrom each other or can be divided into a plurality of combinations ofmap matching of degrees of freedom which are partially different. As anexample of the combinations of map matching of degrees of freedom whichare different from each other, after map matching is performed withrespect to each of (x, y, h) and (z, r, p), two results thereof can becombined to obtain a map matching result approximate to the six degreesof freedom. As another example, after map matching is performed withrespect to each of (x, y, h) and (r, p), the height z value iscalculated by adding a height of a vehicle to a height of a ground (x,y) on which the autonomous vehicle is positioned, and thereby a mapmatching result approximate to the six degrees of freedom can beobtained. As still another example, the map matching can be performedwith respect to each of (x, y) and (r, p, h).

As an example of the combinations of map matching of degrees of freedomwhich are partially different, after map matching is performed withrespect to each of (x, y, h) and (r, p, h), two results thereof can becombined to obtain a map matching result approximate to the six degreesof freedom. As still another example, after the map matching isperformed for each of (x, y, h) and (x, r, p) or (x, y, h) and (y, r,p), two results thereof can be combined.

FIG. 5 illustrates an embodiment according to the present disclosure inwhich a vertical-object part and a horizontal-object part are extractedfrom a second statistical map obtained by a detection sensor and thepart is used in separate map matching process.

The 3D data of the surroundings which is obtained from the detectionsensor contains a horizontal object such as a road or a sidewalk and avertical object such as a tree, a building, a curb, a car, or apasserby. For example, when a 2D plane ((x, y) plane in a case of theorthogonal coordinate system) corresponding to the ground which contains3D points obtained from the detection sensor is divided, and conversioninto the statistical map in which variance values of the z values of the3D points corresponding to respective divided regions are used asstatistical values is performed, a divided region corresponding to avertical object has a high variance value with respect to the height z,and a divided region corresponding to a horizontal object has a lowvariance value. Consequently, the statistical map can be divided into apart of the vertical object and a part of the horizontal objectdepending on the variance value, and the parts can be extracted. Thepart of the vertical object can contain a part having a low variancevalue which is positioned around a part having a high variance value andis positioned within a certain distance such that the vertical objectcan be clearly identified.

In the vertical-object part, an error increases due to a horizontalposition (for example, x and y in the orthogonal coordinate system) anda change (h) in attitude. In the horizontal-object part, an errorincreases due to a vertical position (z in the orthogonal coordinatesystem) and a change (r, p) in attitude. Consequently, in the example ofthe orthogonal coordinate system, the vertical-object part can be usedwhen subset groups of (x, y, h) are matched as major groups, and thehorizontal-object part can be used when subset groups of (z, r, p) arematched as major groups. According to an embodiment of the presentdisclosure, as illustrated in FIG. 5 , while the map matching isperformed with the division of (x, y, h) and (z, r, p), the map matchingcan be performed with respect to (x, y, h) by using the vertical-objectpart, and the map matching can be performed with respect to (z, r, p) byusing the horizontal-object part.

According to another embodiment of the present disclosure, an error ofthe height is not large even when the height z of the autonomous drivingapparatus is not determined by the map matching, and thus the mapmatching can be performed with respect to the vertical-object part andthe horizontal-object part with division into (x, y, h) and (r, p). Inaddition, according to still another embodiment of the presentdisclosure, the map matching with the vertical-object part and thehorizontal-object part can be performed with division into (1) (x, y, h)and (x, r, p), (2) (x, y, h) and (y, r, p), or (3) (x, y, h) and (h, r,p).

FIG. 6 illustrates embodiments according to the present disclosure, ofwhich methods for combining the two map matching results after the mapmatchings are performed in two different groups.

According to an embodiment of the present disclosure illustrated in FIG.6(a), matching B can be performed using a result obtained in matching Aso as to obtain a final map matching result. For example, map matchingwith respect to (x, y, h) is performed in the matching A to obtain avalue of (x′, y′, h′) by which an error is minimized, the value of (x′,y′, h′) by which the error is minimized is assumed in the matching B,and matching with respect to (z, r, p) is performed to finally obtain amap matching result with respect to the six degrees of freedom (x, y, z,h, p, r). Another embodiment of the present disclosure can employ amethod for obtaining final (x′, y′, h′, r′, p′) by storing values of(r′, p′), by which an error is minimized, with respect to respectivevalues of h while map matching with respect to (h, r, p) is performed,then, retrieving the values of (r′, p′) stored with respect to therespective values of h while map matching with respect to (x, y, h) isperformed, and performing map matching with respect to (x, y, h, r′, p′)to find (x′, y′, h′) by which the error is minimized.

According to an embodiment of the present disclosure illustrated in FIG.6(b), the matching A and the matching B can be performed individuallyand results thereof can be finally combined. For example, the matchingwith respect to (x, y, h) is performed in the matching A and matchingwith respect to (z, r, p) is performed in the matching B such that (x′,y′, h′) and (z′, r′, p′), by which the error is minimized, obtained inthe two events of matching can be simply combined to obtain a finalmatching result. According to still another embodiment of the presentdisclosure, map matching with respect to (x, y, h) is performed toobtain (x′, y′, h′), by which the error is minimized, in the matching A,the values (r′, p′), by which the error is minimized, are stored inadvance with respect to respective values of h while map matching withrespect to (r, p, h) is performed in the matching B, and (x′, y′, h′)obtained in the matching A in a combination step and a result (r′, p′,h′) of the matching B which correspond to h acquired in the matching Acan be combined to obtain the final matching result.

According to an embodiment of the present disclosure illustrated in FIG.6(c), the matching A and the matching B can be performed individually,and, based on results thereof, a final map matching result can beobtained using the map matching again. For example, while events of mapmatching are performed with respect to each of (x, y, h) and (r, p, h),the values of (x, y), by which the error is minimized, are stored withrespect to the respective values of h in the matching A, the values of(r, p), by which the error is minimized, are stored with respect to therespective values of h in the matching B, and a value of h, by which atotal error is minimized, can be found in secondary matching.

FIG. 6 illustrates cases of performing map matching with the divisioninto two combinations; however, according to the present disclosure,even in a case of performing the map matching with division into threeor more combinations, a final map matching result can be obtained byexpanding the methods illustrated in FIG. 6 . According to an embodimentof the present disclosure, three events of map matching can besequentially performed, the three events of map matching can besimultaneously performed and results thereof can be combined, the threeevents of map matching can be simultaneously performed and, then, finalmatching can be again performed using results thereof, or two events ofmap matching can be simultaneously performed and the third map matchingcan be finally performed using results thereof. For example, mapmatching can be performed with respect to (x, y, h) and (r, p, h) toobtain a map matching result with respect to (x, y, h, r, p), and thenmap matching of obtaining a value of z, by which the error is minimized,can be performed to finally obtain a map matching result with respect to(x, y, z, h, r, p).

FIG. 7 illustrates a configuration in which a temporary object isremoved from 3D surrounding data obtained by the detection sensor priorto the map matching according to an embodiment of the presentdisclosure.

A process for recognizing a temporary object and removing the temporaryobject before the map matching according to an embodiment of the presentdisclosure illustrated in FIG. 7 is as follows.

First, the 3D data of the surroundings is input from the detectionsensor (Step 710). The temporary objects are removed from the 3D data(Step 720). Through the map matching according to the presentdisclosure, the pose X_(t) of the autonomous driving apparatus isestimated (Step 730). The autonomous driving apparatus is assumed beingpositioned at the pose X_(t), and coordinates of the objects detected inthe surroundings are acquired (Step 740). The temporary objects areidentified by comparing the positions corresponding to the coordinatesof the surrounding objects in the 3D map data (Step 750). Next, theprocess returns to Step 710, and the steps are repeatedly executed.

Here, Step 740 of acquiring the coordinates of the surrounding objectsaround the autonomous driving apparatus cab be subdivided into Step 741of grouping the same object in the 3D data for the surroundings inputfrom the detection sensor and Step 742 of acquiring a coordinate of aregion which the same grouped object occupies.

First, Step 741 of grouping the same object in the 3D data detected withrespect to the surroundings can be executed by various methods. Forexample, objects disposed within a predetermined distance can be groupedas the same object, an object having a continuous outline can be groupedas the same object, or object grouping can be performed using imageprocessing.

In addition, after the grouping, the coordinate of the region which thesame grouped object occupies is acquired (Step 742). Here, thecoordinate of the region which the same grouped object occupies can berepresented by a group of all coordinates at which the object is presentor can be represented by coordinates corresponding to the outline of theobject.

According to an embodiment of the present disclosure, Step 740 ofacquiring the coordinates of surrounding objects around the autonomousdriving apparatus can be executed by extracting the statistical map fromthe 3D surrounding data. In this case, the coordinate of the regionwhich the object occupies can mean coordinates on the 2D plane on whichthe object is positioned. For example, in the case of the orthogonalcoordinate system, the region of grids in which an object is positionedon the (x, y) plane can be represented by the region which the objectoccupies.

Next, the temporary object is identified by comparing the positionscorresponding to the coordinates of the surrounding objects in the 3Dmap data (Step 750), and when the corresponding object is not present ata position corresponding to the coordinates of the surrounding object inthe 3D map data, the corresponding object can be recognized as thetemporary object. For example, when the position corresponding to thecoordinates of one surrounding object in the 3D map data corresponds toa road or a sidewalk, the corresponding surrounding object can berecognized as the temporary object.

According to an embodiment of the present disclosure, it is possible toidentify the position corresponding to the coordinates of thesurrounding objects by using a statistical map extracted from the 3D mapdata. The regions of the surrounding object in the statistical mapcorrespond to the divided regions of the 2D plane in which thesurrounding object is positioned, spatial features of the correspondingdivided regions can be found using the statistical values stored for thecorresponding divided region, and whether the corresponding surroundingobjects in the first statistical map are identified as the temporaryobject can be discerned based on the spatial feature.

According to an embodiment of the present disclosure, in Step 720 ofremoving the temporary object from the 3D surrounding data, datacorresponding to the temporary object can be removed from the 3Dsurrounding data obtained from the detection sensor, or a partcorresponding to the temporary object can be removed from thestatistical map extracted from the 3D surrounding data. In addition,according to another embodiment of the present disclosure, in a casewhere a vertical object and a horizontal object are divided andextracted from the statistical map of the 3D surrounding data, thetemporary object can be removed from the vertical-object part.

FIG. 8 illustrates method for removing a temporary object and a mapmatching according to an embodiment of the present disclosure.

In an embodiment illustrated in FIG. 8 , the 3D data of the surroundingenvironment obtained from the detection sensor is converted into thestatistical map, the statistical map is divided into the vertical-objectpart and the horizontal-object part depending on the variance values ofthe divided regions of the statistical map, and the temporary objectpart is removed from the vertical-object part. Next, the statistical mapis extracted from the 3D map data, the map matching (vertical mapmatching) with respect to the vertical-object part and (x, y, h) isperformed, and the map matching (horizontal map matching) with respectto the horizontal-object part and (r, p, h) is performed. The results ofthe vertical map matching and the horizontal map matching can becombined through various methods as illustrated in FIGS. 6A to 6C, andthe map matching result can be finally obtained with respect to fivedegrees of freedom (x, y, h, r, p). In addition, the height z can bedetermined as a value obtained by adding a constant (height of the car)to a ground height of the 3D map corresponding to corresponding (x, y).

When the pose (X_(t)) of the autonomous driving apparatus is determined,the objects are recognized from the 3D surrounding data obtained fromthe detection sensor, the coordinates of the corresponding objects arecalculated, and whether the corresponding object is the temporary objectis identified by comparing the corresponding coordinate with the 3D mapdata. According to another embodiment of the present disclosure, it ispossible to identify the surrounding objects in the statistical map andto calculate the coordinates of the corresponding objects. In addition,according to the other embodiment of the present disclosure, it ispossible to identify the surrounding objects in the vertical-object partof the statistical map and to calculate the coordinates of thecorresponding objects. In addition, according to an embodiment of thepresent disclosure, when a mean of variance values of the coordinates atwhich the object is positioned in the statistical map of the 3D map iscalculated to be smaller than a predetermined threshold value, theobject can be identified as the temporary object.

The map matching method for autonomous driving and the apparatus thereofaccording to the present disclosure are described as described abovewith reference to the drawings in this application; however, the presentdisclosure is not limited to the drawings and the describedconfiguration method. Various coordinate systems, sensors, algorithms,and devices other than those disclosed in this application can be usedas a configuration of the present disclosure, and the scope of rightthereof is not limited to the configurations and methods disclosed inthis application. Those skilled in the art will understand that variousmodification changes of the present disclosure can be performed within ascope of the objects and effects of the present disclosure. In addition,a singular or plural term in this specification can be construed toinclude both the singular and plural term, unless essential.

According to the map matching method or the apparatus of the presentdisclosure, the following effect is achieved. It is possible to estimatean accurate pose of the autonomous driving apparatus to minimize a poseerror, even with an amount of processing which can be performed in realtime.

In addition, another effect is achieved in that it is possible toefficiently remove a part corresponding to a temporary object from 3Ddata of surroundings recognized by the detection sensor of theautonomous driving apparatus and to more accurately obtain the pose ofthe autonomous driving apparatus which is estimated by the map matching.

While the present disclosure has been described with respect to thespecific embodiments, it will be apparent to those skilled in the artthat various changes and modifications may be made without departingfrom the spirit and scope of the present disclosure as defined in thefollowing claims.

All of the disclosed methods and procedures described in this disclosurecan be implemented, at least in part, using one or more computerprograms or components. These components may be provided as a series ofcomputer instructions on any conventional computer readable medium ormachine readable medium, including volatile and non-volatile memory,such as RAM, ROM, flash memory, magnetic or optical disks, opticalmemory, or other storage media. The instructions may be provided assoftware or firmware, and may be implemented in whole or in part inhardware components such as ASICs, FPGAs, DSPs, or any other similardevices. The instructions may be configured to be executed by one ormore processors or other hardware components which, when executing theseries of computer instructions, perform or facilitate the performanceof all or part of the disclosed methods and procedures.

As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless otherwise indicated. It will befurther understood that the terms “comprises” and/or “comprising,” whenused in this specification, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

Reference throughout the specification to “various embodiments,” “someembodiments,” “one embodiment,” “an embodiment,” “another embodiment,”or the like, means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, appearances of the phrases “variousembodiments,” “some embodiments,” “one embodiment,” “an embodiment,”“another embodiment,” or the like, in places throughout thespecification are not necessarily all referring to the same embodiment.Furthermore, the particular features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments. Thus, theparticular features, structures, or characteristics illustrated ordescribed in connection with one embodiment may be combined, in whole orin part, with the features structures, or characteristics of one or moreother embodiments without limitation. Such modifications and variationsare intended to be included within the scope of the present invention.

What is claimed is:
 1. A map matching system for autonomous driving ofan autonomous driving apparatus, the system comprises: a memory; and oneor more processors in communication with the memory, wherein the one ormore processors are configured to: extract a first statistical map frompoints of map data; extract a second statistical map from pointsobtained by a detection sensor of the autonomous driving apparatus;divide the second statistical map into a vertical-object part and ahorizontal-object part depending on statistical values of respectivedivided regions of the second statistical map; and estimate a pose ofthe autonomous driving apparatus by comparing the horizontal-object partor the vertical-object part with the first statistical map, whereinregions of the statistical maps that have statistical values larger thana predetermined value are extracted as the vertical-object part, andregions of the statistical maps that have statistical values smallerthan the predetermined value are extracted as the horizontal-objectpart.
 2. The system of claim 1, comprising the detection sensor.
 3. Thesystem of claim 1, comprising the autonomous driving apparatus.
 4. Thesystem of claim 1, wherein the statistical values of respective dividedregions of the second statistical map comprises at least one of a mean,a mode, a maximum, a median, a minimum, a range, an interquartile range,a quartile deviation, a variance, a standard deviation, a coefficient ofvariation, and a covariance.
 5. The system of claim 1, in the first andsecond statistical maps, a 3D space is divided in accordance with apredetermined rule, and one or more statistical values of 3D pointscontained in a divided region represent the 3D points in the dividedregion.
 6. The system of claim 5, wherein in the first and secondstatistical maps, the 3D space is divided with respect to a 2D plane andone or more statistical values of the remaining dimension of 3D pointscontained in the divided region are calculated and are represented onthe 2D plane.
 7. The system of claim 6, wherein, in the first and secondstatistical maps, an (x, y) plane is divided into evenly spaced grids inthe Cartesian coordinate system, and one or more statistical values ofheight z values of 3D points corresponding to the respective grids arerepresented on the (x, y) plane.
 8. The system of claim 6, wherein, inthe first and second statistical maps, statistical values of height zvalues corresponding to regions formed by dividing r and θ of an (r, θ)plane evenly in the cylindrical coordinate system are represented on the(r, θ) plane.
 9. The system of claim 6, wherein, in the first and secondstatistical maps, statistical values of respective θ values of 3D pointscorresponding to regions formed by dividing r and ϕ of an (r, ϕ) planeevenly in the spherical coordinate system are represented on the (r, ϕ)plane.
 10. The system of claim 5, wherein the one or more processors areconfigured to detect whether 3D points contained in each divided regionare separated into two or more groups in the statistical maps.
 11. Thesystem of claim 10, wherein the one or more processors are configuredto, responsive to detecting the two or more separated groups of 3Dpoints in a divided region, device the divided region again such thateach one of divided regions contains one group of 3D points only. 12.The system of claim 1, wherein the one or more processors are configuredto perform the map matching based on a location of the autonomousdriving apparatus which is estimated using data obtained by a motionsensor.
 13. The system of claim 1, wherein in the estimating a pose ofthe autonomous driving apparatus, the first statistical map is comparedwith the vertical-object part or the horizontal-object part, based oneach points located in a search range, and the position having a highestmutual similarity is estimated as a result, wherein the position havingthe highest mutual similarity refers to having the lowest error betweenthe first statistical map and the vertical-object part or thehorizontal-object part.
 14. The system of claim 1, wherein in theestimating a pose of the autonomous driving apparatus, a map matching isperformed using the first statistical map and the vertical-object part,and by using the result thereof, a final map matching is performed usingthe first statistical map and the horizontal-object part, or wherein inthe estimating a pose of the autonomous driving apparatus, a mapmatching is performed using the first statistical map and thehorizontal-object part, and by using the result thereof, a final mapmatching is performed using the first statistical map and thevertical-object part.
 15. The system of claim 14, wherein the mapmatching using the vertical-object part is performed with respect to (x,y, h) or a part thereof, and the map matching using thehorizontal-object part is performed with respect to (z, h, r, p) or apart thereof, or wherein in the estimating a pose of the autonomousdriving apparatus, a map matching with respect to (h, r, p) is performedusing the first statistical map and the horizontal-object part to storevalues of (r′, p′) which minimize errors with respect to the respectiveh values, and a map matching with respect to (x, y, h) is performedusing the first statistical map and the vertical-object part to performa map matching with respect to (x, y, h, r′, p′) using the values of(r′, p′) with respect to the respective h values stored in advance. 16.The system of claim 1, wherein in the estimating a pose of theautonomous driving apparatus, after a map matching performed using thefirst statistical map and the horizontal-object part and a map matchingperformed using the first statistical map and the vertical-object part,the two map matching results are combined to obtain a final map matchingresult.
 17. The system of claim 16, wherein in the estimating a pose ofthe autonomous driving apparatus, a map matching with respect to (x, y,h) is performed using the first statistical map and the vertical-objectpart to calculate the value of (x′, y′, h′) which minimize errors, and amap matching of the first statistical map and the horizontal-object partwith respect to (r, p, h) is performed to store values of (r, p) whichminimize errors with respect to the respective h values, and (x′, y′,h′, r′, p′) is estimated as a final position by using (r′, p′)corresponding to h′.
 18. The system of claim 1, wherein the one or moreprocessors are further configured to: acquire coordinates of surroundingobjects around the autonomous driving apparatus; identify a temporaryobject by comparing positions corresponding to the coordinates of thesurrounding objects in the map data in order to identify a partcorresponding to the temporary object; and remove a part correspondingto the temporary object.
 19. The system of claim 18, wherein theacquiring of the coordinates of the surrounding objects around theautonomous driving apparatus further comprises: grouping the pointsindicating the same object of the points input from the detectionsensor, and acquiring the coordinate of the region that the same objectof the grouped points occupies.
 20. The system of claim 18, wherein inthe identifying of the temporary object by comparing the positionscorresponding to the coordinates of the surrounding objects in the mapdata, a spatial feature of the corresponding coordinates is found usingstatistical values corresponding to the coordinates of the surroundingobjects in the first statistical map, and presence of a temporary objectat the corresponding coordinates is identified based on the spatialfeature.